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through the Galaxy Zoo project and its offshoots; (3) the sophistication of modern numerical simulation programs that can make model galaxies that are so realistic that they can be classified like real galaxies; and (4) the advances in artificial neural networks that have made automatic galaxy classification feasible on a scale not previously achieved.
The SDSS will continue to play a role in all of this through its many data releases, the latest of which, DR 16, will occur in December 2019. In general, SDSS images are of very high quality for both qualitative and quantitative studies of galaxies. However, the SDSS footprint has largely excluded the southern sky. The Panoramic Survey Telescope and Rapid Response System (Pann-STARRS; Kaiser et al. 2010) cover the part of the sky north of declination −30°. Although many of the Pan-STARRS images have peculiar defects, many are still very useful. Sophisticated databases in progress, or planned, include the Dark Energy Camera Legacy Survey (DECaLS; Blum et al. 2016), the Kilo-Degree Survey (KiDS; de Jong et al. 2013), the Hyper Suprime-Cam Subaru Strategy Program (HSC-SSP; Aihara et al. 2018), the Large Synoptic Survey Telescope (LSST; ********Ivezi`c et al. 2019), the European Space Agency Euclid mission (Laureijs et al. 2012) and the Wide Field Infrared Survey Telescope (WFIRST; Gehrels et al. 2015). Although the goals of many of these new surveys are mainly cosmology related (with focus on dark energy and dark matter), the public availability of these imaging databases will facilitate deeper, higher resolution studies of both nearby and very distant galaxies.
The Illustris project (Vogelsberger et al. 2014) is one of the most comprehensive simulational surveys of galaxies ever made. The project takes advantage of advances in simulation programming, storage space and computing power, and includes as many physical processes as relevant, such as feedback due to AGN, supernovae, and supermassive black holes, mass and internal dynamics of dark and baryonic matter and the efficacies of star formation, to produce synthetic galaxies at such a realistic level that, once converted into observational units, can match the morphology of many SDSS galaxies. An expert morphologist would be able to examine these artificial galaxies and classify many within the Hubble tuning fork or in some other manner. Snyder et al. (2015) show using non-parametric measures of galaxy morphology that Illustris synthetic galaxies approximate z≈0 galaxies well enough to match the correlation between star formation and galaxy morphology. A great advantage of the project is the ability of the survey to show how different synthetic galaxies evolve under the conditions of the models. For example, followed to high redshifts, the Illustris models reproduce the observed characteristic that galaxies become more irregular at higher redshifts than at lower redshifts (Genel et al. 2014).
Like the SDSS, many of the new digital imaging surveys will provide detailed information on the morphologies of literally millions of galaxies over a wide range of redshift. The classifications of these galaxies are critical to cosmological studies of galaxy formation and evolution. While significant subsets of galaxies are and will continue to be classified visually by single or multiple experts, the vast majority of galaxies will have to be classified in other ways. The Galaxy Zoo project (Lintott et al. 2008) recognized that there will never be enough professional experts to classify hundreds of thousands of galaxies, and instead enlisted the volunteer assistance of tens of thousands of non-expert “citizen scientists” to classify galaxies in enough detail to be cosmologically useful. The Galaxy Zoo Team did not require that volunteers use the CVRHS system to classify galaxies, but instead allowed volunteers to select a few basic characteristics through the use of a small number of buttons in a web interface. How GZ2 classifications compare with some CVRHS classifications was shown in section 1.11. However, as effective as Galaxy Zoo has been for large-scale galaxy classification, there will still be hundreds of thousands or millions of galaxies that will need to be classified.
Dieleman et al. (2015) describe an effective method for automatic classification that uses deep learning with convolutional neural networks. The method inputs multifilter SDSS images of a large sample of galaxies having known morphological classifications. Domínguez Sánchez et al. (2019) use the crowd-sourced GZ2 classifications and the T-classifications of Nair and Abraham (2010) to obtain automatic classifications of 670,000 galaxies.
Many other aspects of galaxy morphology are worth examining; these are described in more detail in the review articles by Buta (2012, 2013).
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